Why the study?
Driving is an essential activity, making research into road safety increasingly relevant.
Can a machine learning algorithm using physiological signals from smart glasses accurately classify road types while driving?
Can a machine learning algorithm using physiological signals from smart glasses accurately classify road types while driving?
A machine learning algorithm using physiological signals from smart glasses can accurately classify road types during real-world driving.
Supports wearable road classification via physiological signals; leaves open validation in larger cohorts before any clinical use.
Driving a car is an activity that became necessary for exploration, even when living in the present world. Research exploring the topic of safety on the roads has therefore become increasingly relevant. In this paper, we propose a recognition algorithm based on physiological signals acquired from JINS MEME ES_R smart glasses (electrooculography, acceleration and angular velocity) to classify four commonly encountered road types: city road, highway, housing estate and undeveloped area. Data from 30 drivers were acquired in real driving conditions. Hand-crafted statistical features were extracted from the physiological signals to train and evaluate a random forest classifier. We achieved an overall accuracy, precision, recall and F1 score of 87.64%, 86.30%, 88.12% and 87.08% on the test dataset, respectively.
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Doniec et al. (2022) studied this question.
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